—As intervened edges are difficult to be determined when intervention method is used for learning the causal relationships of probability model, an active learning method (Structural Intervention Learning of Sensitivity Analysis –SILSA Algorithm) is proposed. SILSA algorithm obtains original network structure based on k2 algorithm, then uses junction tree algorithm to decompose original networks structure and takes local intervention learning in every clique of junction tree, which can decrease the searching extension of intervened edges. Causal Bayesian networks can be learned by Edge-based Interventions when intervened edges are selected. In order to get appropriate intervened edge, sensitivity analysis is used to select the important edge in SILSA algorithm. The efficient of selecting intervened edge is improved. Experimental results show that the effectiveness and performance of SILSA algorithm are better than intervened edges with choosing randomly and passive learning method.
展开▼
机译：- 当干预方法用于学习概率模型的因果关系时，难以确定涉及介入的边缘，提出了一种有源学习方法（灵敏度分析-Silsa算法的结构干预学习）。 Silsa算法获得基于K2算法的原始网络结构，然后使用结树算法来分解原始网络结构，并在结束的每个集团中进行局部干预学习，这可以减少介入边缘的搜索扩展。 Causal Bayesian networks can be learned by Edge-based Interventions when intervened edges are selected.为了获得适当的干预边缘，使用灵敏度分析来选择Silsa算法中的重要边缘。选择干预边缘的有效性得到了改善。实验结果表明，Silsa算法的有效性和性能优于选择随机和被动学习方法的介入边缘。
展开▼
